Real-time Superpixel Segmentation by DBSCAN Clustering Algorithm (original) (raw)

mDBSCAN: Real Time Superpixel Segmentation by DBSCAN Clustering based on Boundary Term

Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, 2019

mDBSCAN is an improved version of DBSCAN (Density Based Spatial Clustering of Applications with Noise) superpixel segmentation. Unlike DBSCAN algorithm, the proposed algorithm has an automatic threshold based on the colour and gradient information. The proposed algorithm performs under different colour space such as RGB, Lab and grey images using a novel distance measurement. The experimental results demonstrate that the proposed algorithm outperforms the state of the art algorithms in terms of boundary adherence and segmentation accuracy with low computational cost (30 frames/s).

Image Segmentation Methods Based on Superpixel Techniques: A Survey

2020

There is a growing demand for image processing in a wide range of applications such as photography, robotics, television, remote sensing, industrial inspection, and medical diagnosis. This study overviews some of the existing image segmentation methods that focus on producing superpixels. A superpixel or segment is a homogeneous, local coherent structure that specifies information oversampling or scales resolutions. There are many image segmentation or superpixelization methods which divide color image with different techniques according to their characteristics and parameters as image acquisition might be seriously affected by many factors such as light and shadow. Several image segmentation algorithms were investigated in image processing research for creating superpixels that may lack the ability to control the size, number, and compactness of segments. Superpixel generation algorithms can be categorized into graph-based methods and gradient-ascent

A Survey on Different Methods for Superpixel Segmentation

2019

Image segmentation is an important part of image analysis process, since it differentiates between the salient objects and the other objects or from their background. It is the process of dividing digital image into multiple segments and the main aim of segmentation is to pinpoint objects and boundaries. There are different methods for segmenting image, here we are considering the concept of superpixels inorder to segment image. Superpixel can mainly accelerate the successive processing since the superpixels of an image carry more information than a normal pixel. This paper deals with detailed survey on different superpixel segmentation techniques. IndexTerms: Salient object, Superpixel, Discriminability. ________________________________________________________________________________________________________

Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering

IEEE Access, 2020

Although automatic fuzzy clustering framework (AFCF) based on improved density peak clustering is able to achieve automatic and efficient image segmentation, the framework suffers from two problems. The first one is that the adaptive morphological reconstruction (AMR) employed by the AFCF is easily influenced by the initial structuring element. The second one is that the improved density peak clustering using a density balance strategy is complex for finding potential clustering centers. To address these two problems, we propose a fast and automatic image segmentation algorithm using superpixel-based graph clustering (FAS-SGC). The proposed algorithm has two major contributions. First, the AMR based on regional minimum removal (AMR-RMR) is presented to improve the superpixel result generated by the AMR. The binary morphological reconstruction is performed on a regional minimum image, which overcomes the problem that the initial structuring element of the AMR is chosen empirically, since the geometrical information of images is effectively explored and utilized. Second, we use an eigenvalue gradient clustering (EGC) instead of improved density peak (DP) algorithms to obtain potential clustering centers, since the EGC is faster and requires fewer parameters than the DP algorithm. Experiments show that the proposed algorithm is able to achieve automatic image segmentation, providing better segmentation results while requiring less execution time than other state-of-the-art algorithms. INDEX TERMS Image segmentation, fuzzy clustering, graph clustering, density peak (DP) algorithm.

COMPARATIVE ANALYSIS OF SUPERPIXEL SEGMENTATION METHODS

Superpixel segmentation showed to be a useful preprocessing step in many computer vision applications. Superpixel's purpose is to reduce the redundancy in the image and increase efficiency from the point of view of the next processing task. This led to a variety of algorithms to compute superpixel segmentations, each with individual strengths and weaknesses. Many methods for the computation of superpixels were already presented. A drawback of most of these methods is their high computational complexity and hence high computational time consumption. K mean based SLIC method shows better performance as compare to other while evaluating on the bases of under segmentation error and boundary recall, etc parameters.

Superpixel-based Fast Fuzzy C-Means Clustering for Color Image Segmentation

IEEE Transactions on Fuzzy Systems

A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely used for grayscale and color image segmentation. However, most of them are timeconsuming and unable to provide desired segmentation results for color images due to two reasons. The first one is that the incorporation of local spatial information often causes a high computational complexity due to the repeated distance computation between clustering centers and pixels within a local neighboring window. The other one is that a regular neighboring window usually breaks up the real local spatial structure of images and thus leads to a poor segmentation. In this work, we propose a superpixel-based fast FCM clustering algorithm (SFFCM) that is significantly faster and more robust than stateof-the-art clustering algorithms for color image segmentation. To obtain better local spatial neighborhoods, we firstly define a multiscale morphological gradient reconstruction (MMGR) operation to obtain a superpixel image with accurate contour. In contrast to traditional neighboring window of fixed size and shape, the superpixel image provides better adaptive and irregular local spatial neighborhoods that are helpful for improving color image segmentation. Secondly, based on the obtained superpixel image, the original color image is simplified efficiently and its histogram is computed easily by counting the number of pixels in each region of the superpixel image. Finally, we implement FCM with histogram parameter on the superpixel image to obtain the final segmentation result. Experiments performed on synthetic images and real images demonstrate that the proposed algorithm provides better segmentation results and takes less time than state-of-the-art clustering algorithms for color image segmentation.

Superpixel Segmentation Using Dynamic and Iterative Spanning Forest

IEEE Signal Processing Letters, 2020

As constituent parts of image objects, superpixels can improve several higher-level operations. However, image segmentation methods might have their accuracy seriously compromised for reduced numbers of superpixels. We have investigated a solution based on the Iterative Spanning Forest (ISF) framework. In this letter, we present Dynamic ISF (DISF)-a method based on the following steps. (a) It starts from an image graph and a seed set with considerably more pixels than the desired number of superpixels. (b) The seeds compete among themselves, and each seed conquers its most closely connected pixels, resulting in an image partition (spanning forest) with connected superpixels. In step (c), DISF assigns relevance values to seeds based on superpixel analysis and removes the most irrelevant ones. Steps (b) and (c) are repeated until the desired number of superpixels is reached. DISF has the chance to reconstruct relevant edges after each iteration, when compared to region merging algorithms. As compared to other seed-based superpixel methods, DISF is more likely to find relevant seeds. It also introduces dynamic arc-weight estimation in the ISF framework for more effective superpixel delineation, and we demonstrate all results on three datasets with distinct object properties.

Iterative Boundaries Implicit Identification for Superpixels Segmentation: A Real-Time Approach

IEEE Access

Superpixel algorithms group visually coherent pixels and form an alternative representation of the regular structure of the pixel grid. This fundamental low-level computer vision preprocessing step greatly reduces the complexity of subsequent image processing tasks. However, most of the existing methods suffer from very high calculation costs which makes them quite unsuitable for time-sensitive applications. In this paper, we propose a new superpixel segmentation method, named IBIS for Iterative Boundaries implicit Identification for superpixels segmentation, that implicitly identifies the boundaries between superpixels and performs the segmentation using only a fraction of the pixels of the input image, thereby reducing the complexity and computation time. The results obtained during the experiments show that the segmentation quality of IBIS is comparable to that of state of the art methods with a computation time divided by a factor of 8 without parallelization of the processing for low resolution images (e.g., 320 × 240 pixels) as usually provided in public data sets. We also present and comprehensively evaluate the GPU variant of IBIS named IBIScuda that allows an optimal exploitation of the available resources considering the limited bandwidth between CPU and GPU memories.

Adaptive strategy for superpixel-based region-growing image segmentation

Journal of Electronic Imaging

This work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions. This approach raises two key issues: (1) how to compute the similarity between superpixels in order to perform accurate merging and (2) in which order those superpixels must be merged together. In this perspective, we firstly introduce a robust adaptive multi-scale superpixel similarity in which region comparisons are made both at content and common border level. Secondly, we propose a global merging strategy to efficiently guide the region merging process. Such strategy uses an adpative merging criterion to ensure that best region aggregations are given highest priorities. This allows to reach a final segmentation into consistent regions with strong boundary adherence. We perform experiments on the BSDS500 image dataset to highlight to which extent our method compares favorably against other well-known image segmentation algorithms. The obtained results demonstrate the promising potential of the proposed approach.

Applications and Datasets for Superpixel Techniques A Survey

2020

The use of superpixels instead of pixels can significantly improve the speed of the current pixel-based algorithms, and can even produce better results in many applications such as robotics, remote sensing, industrial inspection, and medical diagnosis. Two main tasks related to vision could benefit from superpixels, named object class segmentation and medical image segmentation. In both cases, superpixels can increase performance significantly and also reduce the computational cost. In addition to superpixel applications, various datasets were employed for the evaluation of the superpixel algorithms. This work aims to survey the recent applications and the most common datasets that can be used based on superpixel techniques.